This thesis reports the latest measurements on one of the leading dark matter searches conducted by the Compact Muon Solenoid (CMS) experiment at CERN, leading to some of the most stringent constraints on hypothesized interactions between the Higgs boson and dark matter. The thesis also includes further research about the future outlook of the experiment, including exploratory research on the adaptation of deep learning models in future dark matter analyses to improve analysis sensitivity, and the design of a new type of data processing hardware to be used in the next phase of the CMS experiment.

Nominated as an outstanding PhD thesis by Boston University, USA Reports the latest measurements from a leading search for dark matter at CERN Describes research on the adaptation of deep learning models for future dark matter analyses

Autorentext

Graduated from Bogazici University in Istanbul, Turkey with a degree in civil engineering and physics. Pursued a PhD in Boston University, contributing to dark matter searches and tracker hardware upgrade projects in the CMS experiment at CERN. Currently working as a machine learning engineer.

Inhalt

Chapter 1: Theoretical Background.- Chapter 2: Experimental Apparatus.- Chapter 3: Data Analysis Strategy.- Chapter 4: Results.- Chapter 5: Outlook.